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I got a 80% return with a simple modification of the sample algorithm.

Does it make sense?

Clone Algorithm
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
  # For this example, we're going to write a simple momentum script.  When the 
  # stock goes up quickly, we're going to buy; when it goes down quickly, we're
  # going to sell.  Hopefully we'll ride the waves.

  # To run an algorithm in Quantopian, you need two functions: initialize and 
  # handle_data.

def initialize(context):
  # This initialize function sets any data or variables that you'll use in
  # your algorithm.  For instance, you'll want to define the security (or 
  # securities) you want to backtest.  You'll also want to define any 
  # parameters or values you're going to use.

  # In our example, we're looking at Apple.  If you re-type this line 
  # yourself, you'll see the auto-complete that is available for the 
  # security ID.
  context.aapl = sid(24)
  # In these two lines, we set the maximum and minimum we want our algorithm 
  # to go long or short our security.  You don't have to set limits like this
  # when you write an algorithm, but it's good practice.
  context.max_notional = 1000000.1
  context.min_notional = -1000000.0

def handle_data(context, data):
  # This handle_data function is where the real work is done.  Our data is
  # minute-level tick data, and each minute is called a frame.  This function
  # runs on each frame of the data.
  # We've built a handful of useful data transforms for you to use.  In this 
  # line, we're computing the volume-weighted-average-price of the security 
  # defined above, in the context.aapl variable.  For this example, we're 
  # specifying a three-day average.
  vwap = data[context.aapl].vwap(30)
  # We need a variable for the current price of the security to compare to
  # the average.
  price = data[context.aapl].price
  # Another powerful built-in feature of the Quantopian backtester is the
  # portfolio object.  The portfolio object tracks your positions, cash,
  # cost basis of specific holdings, and more.  In this line, we calculate
  # how long or short our position is at this minute.   
  notional = context.portfolio.positions[context.aapl].amount * price
  # This is the meat of the algorithm, placed in this if statement.  If the
  # price of the security is .5% less than the 3-day volume weighted average
  # price AND we haven't reached our maximum short, then we call the order
  # command and sell 100 shares.  Similarly, if the stock is .5% higher than
  # the 3-day average AND we haven't reached our maximum long, then we call
  # the order command and buy 100 shares.     
  if price < vwap * 0.995 and notional > context.min_notional:
  elif price > vwap * 1.005 and notional < context.max_notional:
This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes.
There was a runtime error.
4 responses

@Zhiyong, it's possible to fit any curve by tweaking backtest numbers. This result is not generalizable to other stocks or time periods.

Right - you may have over fitted your data or done some "data mining".

Here's how I explain this to people: There are a million different ways to write an algorithm that goes long on Apple in 2008. If you did that, you made money - Apple went up. You need to write an algorithm that is smart enough to pick Apple out of the sea of choices in 2008, OR you need to write an algorithm that can make money even when the underlying stock isn't a rocketship.

Backtesting is great because it lets your rule out the obvious losers. But when you get an algorithm that looks good, that's when it gets even harder. You have to scrutinize it again and make sure you're not fooling yourself. There are a couple good ways to do that. One is to hold back some data, called "out of sample data" and then at the very end, test the out-of-sample data. The other is paper trading. You can never overfit papertrading.


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^^Well said Dan. I also notice that algorithms that switch to cash or commodities just before the 2008 crash seem to beat benchmark by upwards of 100% but might have been trailing before that.